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ICHNet: Intracerebral Hemorrhage (ICH) Segmentation Using Deep Learning

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11383)


We develop a deep learning approach for automated intracerebral hemorrhage (ICH) segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by integrating dilated convolution neural network (CNN) with hypercolumn features where a modest number of pixels are sampled and corresponding features from multiple layers are concatenated. Due to freedom of sampling pixels rather than image patch, this model trains within the brain region and ignores the CT background padding. This boosts the convergence time and accuracy by learning only healthy and defected brain tissues. To overcome the class imbalance problem, we sample an equal number of pixels from each class. We also incorporate 3D conditional random field (3D CRF) to smoothen the predicted segmentation as a post-processing step. ICHNet demonstrates 87.6% Dice accuracy in hemorrhage segmentation, that is comparable to radiologists.


  • Intracerebral hemorrhage
  • Stroke
  • Deep learning
  • Convolutional neural network
  • PixelNet
  • Conditional Random Field
  • Hypercolumn

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  • DOI: 10.1007/978-3-030-11723-8_46
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This work is supported by the Singapore Academic Research Fund under Grant R-397-000-227-112, NUSRI China Jiangsu Provincial Grant BK20150386 and BE2016077 and NMRC Bedside & Bench under grant R-397-000-245-511 awarded to Dr. Hongliang Ren.

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Islam, M., Sanghani, P., See, A.A.Q., James, M.L., King, N.K.K., Ren, H. (2019). ICHNet: Intracerebral Hemorrhage (ICH) Segmentation Using Deep Learning. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11383. Springer, Cham.

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